The present invention relates generally to a method for reconstructing images of coronary vessels and more specifically to a method for three-dimensional (3-D) reconstruction of coronary vessels from two two-dimensional biplane projection images.
Several investigators have reported computer assisted methods for estimation of the 3-D coronary arteries from biplane projection data. These known methods are based on the known or standard X-ray geometry of the projections, placement of landmarks, known vessel shape, and on iterative identification of matching structures in two or more views. Such methods are described in a publication entitled "3-D digital subtraction angiography," IEEE Trans. Med. Imag., vol. MI-1, pp. 152-158, 1982 by H. C. Kim, B.G. Min, T. S. Lee, et. al. and in a publication entitled "Methods for evaluating cardiac wall motion in 3-D using bifurcation points of the coronary arterial tree," Invest. Radiology, January-February pp. 47-56, 1983 by M. J. Potel, J. M. Rubin, and S. A. Mackay, et al. Because the computation was designed for predefined views only, it is not suitable to solve the reconstruction problem on the basis of two projection images acquired at arbitrary and unknown relative orientations.
Another known method is based on motion and multiple views acquired in a single-plane imaging system. Such a method is described in a publication entitled "Reconstructing the 3-d medial axes of coronary arteries in single-view cineangiograms," IEEE Trans. MI, vol. 13, no. 1, pp. 48-60, 1994 by T. V. Nguyen and J. Sklansky uses motion transformations of the heart model. However, the motion transformations of the heart model consist only of rotation and scaling. By incorporation of the center-referenced method, initial depth coordinates, and center coordinates, a 3-D skeleton of the coronary arteries was obtained. However, the real heart motion during the contraction involves five specific movements: translation, rotation, wringing, accordion-like motion, and movement toward the center of the ventricular chamber. Therefore, the model employed is not general enough to portray the true motion of the heart, especially toward the end-systole.
Knowledge-based or rule-based systems have been proposed for 3-D reconstruction of coronary arteries by use of a vascular network model. One such knowledge-based system is described in a publication entitled "An expert system for the labeling and 3-D reconstruction of the coronary arteries from two projections," International Journal of Imaging, Vol. 5, No. 2-3, pp. 145-154, 1990 by Smets, Vandewerf, Suctens, and Oosterlinck. Because the rules or knowledge base were organized for certain specific conditions, it does not generalize the 3-D reconstruction process to arbitrary projection data. In other knowledge-based systems, the 3-D coronary arteries were reconstructed from a set of X-ray perspective projections by use of an algorithm from computed tomography. Due to the motion of the heart and only a limited number of projections (four or six), accurate reconstruction and quantitative measurement are not easily achieved.
Closed-form solutions of the 3-D reconstruction problem using a linear approach was a significant development and is described in, for example, a publication entitled "Determining 3-d motion and structure of a rigid body using the spherical projection," CVGIP, vol. 21, pp. 21-32, 1983 by B. L. Yen and T. S. Huang. Unfortunately, actual data is always corrupted by noise or errors and the linear approach based techniques may not be sufficiently accurate when using noisy data. Hence, optimal estimation has been explicitly investigated. Additionally, U.S. Pat. No. 4,875,165 entitled Method for Determination of 3-D Structures in Biplane Angiography issued in the name of Fencil et al. also has significant drawbacks.
Use of a two-step method is known for producing an optimal estimation for a 3-D structure based on maximum-likelihood and minimum-variance estimation. In these techniques, for example, two publications entitled "Optimal motion and structure estimation," IEEE Trans. on PAMI, Vol. 15, no. Sep. 9, 1993, pp. 864-884, and "Structure from motion using the reconstruction and projection technique," Proc. IEEE Workshop Computer Vision, November 1987, pp. 345-348, image error was employed in the objective function for a non-constricted minimization process. Preliminary estimates computed by a linear algorithm were used as initial estimates for the process of optimal estimation. However, if the initial solution from the linear approach is not sufficient, (e.g., with more than 2 pixels=0.6 mm error in the input 2-D image data), the iterative minimization process at the second step may become trapped in a local minimum due to a lack of prior information concerning the variables to be optimized.
Accordingly, it is an object of the present invention to substantially overcome the above-described problems.
It is another object of the present invention to provide a method for reconstructing three-dimensional coronary structures given two-dimensional projection images.
It is a further object of the present invention to provide a method for reconstructing three-dimensional coronary structures given two-dimensional projection images where the relative orientation between the two-dimensional images is unknown.
It is also an object of the present invention to provide a method for reconstructing an optimal visual three-dimensional representation of coronary structures where vessel overlap and vessel foreshortening are minimized.
It is still an object of the present invention to provide a method for reconstructing an optimal visual three-dimensional representation of coronary structures where the orientation parameters are used to produce further images of the target object